A UN scientific panel urges governments to adopt precautionary AI safeguards rather than wait for definitive proof of harm — a stance with major implications for deepfake and synthetic media regulation worldwide.
New research explores how large language models generate scenario-based fake news through discourse structures — and how detectors can be trained to catch it. A closer look at the arms race between synthetic text generation and authenticity.
AMD's move toward Taalas-style hardwired AI inference silicon challenges the GPU's dominance. Here's why custom chips baking models directly into silicon could reshape the economics of running generative AI — including video and voice models.
A new benchmark systematically tests whether large language models comply with China's AI-generated content regulations, revealing gaps in how models handle labeling, safety, and legal requirements for synthetic media.
New research reveals that text watermarking schemes designed to identify AI-generated content can inadvertently make language models more susceptible to adversarial prompt attacks, exposing a critical tension between authenticity tooling and model security.
Model collapse is the slow-motion degradation that happens when AI models train on AI-generated data. As synthetic media floods the web, future models risk losing diversity, accuracy, and quality in a compounding feedback loop.
As AI video models like Sora, Veo, and Runway close the quality gap, the true production bottleneck has shifted to orchestration — stitching together generation, editing, audio, and consistency into coherent pipelines that scale.
NVIDIA introduces NeMo Data Designer, an extensible framework for generating multimodal synthetic data at scale — a foundation for training the next generation of AI video, image, and audio models.
Nunchux AI's VC-Attention is a training-free, low-bit attention kernel that accelerates video diffusion transformers without retraining—slashing inference costs for AI video generation while preserving output quality.
New research shows how small video-language models can gain advanced reasoning through synthetic chain-of-thought distillation and difficulty-aware fine-tuning, closing the gap with large models at a fraction of the compute cost.
Open-weight Chinese models from labs like DeepSeek and Alibaba are rapidly narrowing the performance gap with Silicon Valley's closed frontier systems—reshaping the economics of AI and the tools that power synthetic media generation.
Sakana AI researchers unveil PC-ALM, a layer-local training method that replaces global backpropagation and successfully trains 1000-layer networks, hinting at more scalable and parallelizable deep learning.